Establishing Disorder-Specific and Transdiagnostic Neural Features of Psychiatric Disorders Through Large-Scale Functional Magnetic Resonance Imaging Meta-Analyses
Introduction Meta-analyses of functional magnetic resonance imaging (fMRI) studies have been used to elucidate the most reliable neural features associated with various psychiatric disorders. However, it has not been well-established whether each of these neural features is linked to a specific dis...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2023-03-01
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| Series: | European Psychiatry |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0924933823011562/type/journal_article |
| _version_ | 1827753524353564672 |
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| author | C. H. Miller E. Pritchard S. Saravia M. Duran S. L. Santos J. P. Hamilton D. W. Hedges I. H. Gotlib M. D. Sacchet |
| author_facet | C. H. Miller E. Pritchard S. Saravia M. Duran S. L. Santos J. P. Hamilton D. W. Hedges I. H. Gotlib M. D. Sacchet |
| author_sort | C. H. Miller |
| collection | DOAJ |
| description |
Introduction
Meta-analyses of functional magnetic resonance imaging (fMRI) studies have been used to elucidate the most reliable neural features associated with various psychiatric disorders. However, it has not been well-established whether each of these neural features is linked to a specific disorder or is transdiagnostic across multiple disorders and disorder categories, including mood, anxiety, and anxiety-related disorders.
Objectives
This project aims to advance our understanding of the disorder-specific and transdiagnostic neural features associated with mood, anxiety, and anxiety-related disorders as well as to refine the methodology used to compare multiple disorders.
Methods
We conducted an exhaustive PubMed literature search followed by double-screening, double-extraction, and cross-checking to identify all whole-brain, case-control fMRI activation studies of mood, anxiety, and anxiety-related disorders in order to construct a large-scale meta-analytic database of primary studies of these disorders. We then employed multilevel kernel density analysis (MKDA) with Monte-Carlo simulations to correct for multiple comparisons as well as ensemble thresholding to reduce cluster size bias to analyze primary fMRI studies of mood, anxiety, and anxiety-related disorders followed by application of triple subtraction techniques and a second-order analysis to elucidate the disorder-specificity of the previously identified neural features.
Results
We found that participants diagnosed with mood, anxiety, and anxiety-related disorders exhibited statistically significant (p < .05 – 0.0001; FWE-corrected) differences in neural activation relative to healthy controls throughout the cerebral cortex, limbic system, and basal ganglia. In addition, each of these psychiatric disorders exhibited a particular profile of neural features that ranged from disorder-specific, to category-specific, to transdiagnostic.
Conclusions
These findings indicate that psychiatric disorders exhibit a complex profile of neural features that vary in their disorder-specificity and can be detected with large-scale fMRI meta-analytic techniques. This approach has potential to fundamentally transform neuroimaging investigations of clinical disorders by providing a novel procedure for establishing disorder-specificity of observed results, which can be then used to advance our understanding of individual disorders as well as broader nosological issues related to diagnosis and classification of psychiatric disorders.
Disclosure of Interest
None Declared |
| first_indexed | 2024-03-11T07:36:25Z |
| format | Article |
| id | doaj.art-f45a9aa59a194fb6beac7a85528887ac |
| institution | Directory Open Access Journal |
| issn | 0924-9338 1778-3585 |
| language | English |
| last_indexed | 2024-03-11T07:36:25Z |
| publishDate | 2023-03-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | European Psychiatry |
| spelling | doaj.art-f45a9aa59a194fb6beac7a85528887ac2023-11-17T05:09:32ZengCambridge University PressEuropean Psychiatry0924-93381778-35852023-03-0166S547S54810.1192/j.eurpsy.2023.1156Establishing Disorder-Specific and Transdiagnostic Neural Features of Psychiatric Disorders Through Large-Scale Functional Magnetic Resonance Imaging Meta-AnalysesC. H. Miller0E. Pritchard1S. Saravia2M. Duran3S. L. Santos4J. P. Hamilton5D. W. Hedges6I. H. Gotlib7M. D. Sacchet8Department of Psychology, California State University, Fresno, United StatesDepartment of Psychology, California State University, Fresno, United StatesDepartment of Psychology, California State University, Fresno, United StatesDepartment of Psychology, California State University, Fresno, United StatesDepartment of Psychology, California State University, Fresno, United StatesDepartment of Biomedical and Clinical Sciences, Linköping University, Linköping, SwedenDepartment of Psychology, Brigham Young University, ProvoDepartment of Psychology, Stanford University, StanfordDepartment of Psychiatry, Harvard University, Cambridge, United States Introduction Meta-analyses of functional magnetic resonance imaging (fMRI) studies have been used to elucidate the most reliable neural features associated with various psychiatric disorders. However, it has not been well-established whether each of these neural features is linked to a specific disorder or is transdiagnostic across multiple disorders and disorder categories, including mood, anxiety, and anxiety-related disorders. Objectives This project aims to advance our understanding of the disorder-specific and transdiagnostic neural features associated with mood, anxiety, and anxiety-related disorders as well as to refine the methodology used to compare multiple disorders. Methods We conducted an exhaustive PubMed literature search followed by double-screening, double-extraction, and cross-checking to identify all whole-brain, case-control fMRI activation studies of mood, anxiety, and anxiety-related disorders in order to construct a large-scale meta-analytic database of primary studies of these disorders. We then employed multilevel kernel density analysis (MKDA) with Monte-Carlo simulations to correct for multiple comparisons as well as ensemble thresholding to reduce cluster size bias to analyze primary fMRI studies of mood, anxiety, and anxiety-related disorders followed by application of triple subtraction techniques and a second-order analysis to elucidate the disorder-specificity of the previously identified neural features. Results We found that participants diagnosed with mood, anxiety, and anxiety-related disorders exhibited statistically significant (p < .05 – 0.0001; FWE-corrected) differences in neural activation relative to healthy controls throughout the cerebral cortex, limbic system, and basal ganglia. In addition, each of these psychiatric disorders exhibited a particular profile of neural features that ranged from disorder-specific, to category-specific, to transdiagnostic. Conclusions These findings indicate that psychiatric disorders exhibit a complex profile of neural features that vary in their disorder-specificity and can be detected with large-scale fMRI meta-analytic techniques. This approach has potential to fundamentally transform neuroimaging investigations of clinical disorders by providing a novel procedure for establishing disorder-specificity of observed results, which can be then used to advance our understanding of individual disorders as well as broader nosological issues related to diagnosis and classification of psychiatric disorders. Disclosure of Interest None Declaredhttps://www.cambridge.org/core/product/identifier/S0924933823011562/type/journal_article |
| spellingShingle | C. H. Miller E. Pritchard S. Saravia M. Duran S. L. Santos J. P. Hamilton D. W. Hedges I. H. Gotlib M. D. Sacchet Establishing Disorder-Specific and Transdiagnostic Neural Features of Psychiatric Disorders Through Large-Scale Functional Magnetic Resonance Imaging Meta-Analyses European Psychiatry |
| title | Establishing Disorder-Specific and Transdiagnostic Neural Features of Psychiatric Disorders Through Large-Scale Functional Magnetic Resonance Imaging Meta-Analyses |
| title_full | Establishing Disorder-Specific and Transdiagnostic Neural Features of Psychiatric Disorders Through Large-Scale Functional Magnetic Resonance Imaging Meta-Analyses |
| title_fullStr | Establishing Disorder-Specific and Transdiagnostic Neural Features of Psychiatric Disorders Through Large-Scale Functional Magnetic Resonance Imaging Meta-Analyses |
| title_full_unstemmed | Establishing Disorder-Specific and Transdiagnostic Neural Features of Psychiatric Disorders Through Large-Scale Functional Magnetic Resonance Imaging Meta-Analyses |
| title_short | Establishing Disorder-Specific and Transdiagnostic Neural Features of Psychiatric Disorders Through Large-Scale Functional Magnetic Resonance Imaging Meta-Analyses |
| title_sort | establishing disorder specific and transdiagnostic neural features of psychiatric disorders through large scale functional magnetic resonance imaging meta analyses |
| url | https://www.cambridge.org/core/product/identifier/S0924933823011562/type/journal_article |
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